Digital Elevation Model Images Research Articles

Space/ground-borne techniques and petrographic microscopic dissection for geologic mapping in Gabal Ras Abda area, Northeastern Desert, Egypt

BackgroundGabal Ras Abda area as a part of the Red Sea Mountain range, is characterized by inaccessible and rugged terrains. The exposed rock units are hardly followed in the field because of the rigid topography. Thus, the present work proposes and develops an integrated approach to map the exposed rock units and extract the geologic structures using satellite imagery data followed by both field and petrographic verification, saving time, efforts and cost.ResultsTo achieve the target, both the measured spectral signature curves with Landsat-8 and Sentinel-2A data were used to develop and create the most enhanced Band Ratios and Principal Components for lithological discrimination and mapping which were (((Band7 + Band 4)/(Band 7), (Band 2)/(Band 2 + Band 5) and (Band 5) in RGB) and ((Band 9 + Band 11 + Band 12)/(Band 1), (Band 4 − Band 2) and (Band 11/Band 6) + (Band 6) in RGB)) with Principal Component Bands ((PC1, PC2 and PC3 in RGB) and (PC3, PC2 and PC1 in RGB)), respectively. Also, georeferenced Google Earth Pro, panchromatic band of Landsat-8 and ALOS PALSAR Digital Elevation Model images were used to extract the structural lineaments. Geologic, petrographic and field structural studies were emphasized the remote sensing results, indicating that the main rock types cropped out in Ras Abda area from the oldest to the youngest are older granitoids (quartz-diorites, tonalites and granodiorites), Dokhan volcanics (andesites, rhyodacites, rhyolites and their related tuffs), younger gabbros, younger granites (monzogranites, syenogranites and alkali-feldspar granites), post-granite dykes and offshoots (acidic, microgranitic and basic types) and Phanerozoic sedimentary rocks. Also, the study emphasized that the E-W trend is the main structural trend controlling the investigated area followed by WNW-ESE and NE-SW directions.ConclusionsThe results of remote sensing achieved compliance with the geologic, petrographic and structural investigation through distinctly differentiating the different rocks and extracting the lineaments, indicating the accuracy of the remote sensing results and emphasizing their importance and effective role in producing a precise and highly accurate geologic map.Graphical abstract

SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network

To address key issues with traditional landform classification methods that impact the integrity and continuity of the analysis area, as well as neglecting neighboring features, We presents a novel approach called superpixel-based graph convolutional network (SGCN) for automatic classification of digital elevation model (DEM) landform. The SGCN uses superpixel segmentation and graph convolutional networks (GCNs). Specifically, we employ the Simple Linear Iterative Clustering (SLIC) algorithm to perform superpixel segmentation on the DEM image and convert the resulting distribution of superpixels in the 2D image into a graph structure, where nodes are labeled. We then analyze the geographic features and external neighborhood relationships within each DEM superpixel to obtain corresponding node information for the graph structure. Finally, the GCN model is trained according to the graph structure, node feature information, and node label, enabling automatic recognition of node landform types. Importantly, SGCN accounts for the multi-order neighborhood features of the basic analysis unit and expresses the DEM micro-features relatively macroscopically. Experimental results demonstrate that compared with pixel-level semantic segmentation methods like U-Net, FCN, and SegNet, the SGCN significantly improves DEM landform classification accuracy by 17.84%, 33.22%, and 30.60%, respectively. Moreover, SGCN better preserves the integrity and continuity of features inside and outside the analysis area, overcoming the limitation of traditional methods that use regular grids as the basic analysis unit, which can compromise landform edge continuity.

Deep learning for check dam area extraction with optical images and digital elevation model: A case study in the hilly and gully regions of the Loess Plateau, China

AbstractCheck dams are widely used on the Loess Plateau of China to control soil and water loss, develop agricultural land and improve watershed ecology. Detailed information on the spatial distribution of check dams and the area of dam land is critical for quantitatively evaluating hydrological and ecological effects, planning the construction of new dams and repairing damaged dams. Therefore, this research presents a method that integrates deep learning and geospatial analysis to facilitate the extraction of check dam areas in broad areas from high‐resolution Gaofen‐2 (GF‐2) multispectral imageries, including red (R), green (G), blue (B) and near‐infrared (NIR) bands and digital elevation model (DEM). First, we generated three datasets with different band combinations (RGB, RGB + NIR and RGB + DEM) using GF‐2 remote sensing images combined with Advanced Land Observing Satellite—the Phased Array type L‐band Synthetic Aperture Radar DEM data to determine the optimal data combination for dam area extraction. Next, four widely used semantic segmentation networks—Fully Convolutional Network (FCN), U‐Net, PSPNet and DeepLabv3+—were modified to support the arbitrary number of input channels and evaluated for check dam area extraction. Finally, the check dam candidate areas in the Yan River basin were extracted from DEM using geospatial analysis to optimize the dam area extraction results. The results showed that all deep learning (DL) models could extract dam areas quickly and accurately with mean intersection over union and overall accuracy values >85% and >98%, respectively. PSPNet had the best performance for testing datasets with different band combinations. We also found that the DL models in the RGB + DEM images had the best remote sensing image segmentation results, avoiding many miss‐classified pixels. The F1 scores in the RGB + DEM test dataset for FCN U‐Net, PSPNet and DeepLabv3+ reached up to 92%, or 3.3%, 2.3%, 1.9% and 3.1% higher than the corresponding values for RGB images. Potential check dam candidate regions (~3969 km2) were obtained for application analysis, which reduced the original area by 50%. The DL models for the RGB + DEM images were applied in the dam candidate regions, extracting 91.9 km2 of agricultural production dam land and 10.6 km2 of runoff and sediment silted dam land. The extraction results will facilitate quantitative analyses of check dams, improve the management of these structures and promote the efficiency of controlling soil losses.

The Use of Sentinel-2A Images to Estimate Potential Flood Risk With A Multi-Index Approach in The Mempawah Watershed

Natural disasters in Indonesia have become an annual cycle, an example is flooding. This study aims to determine the flood risk potential in the Mempawah watershed and the places likely to be flooded. The method used was a survey and interpretation of secondary data from topographic maps, Sentinel-2A images, and Digital Elevation Model images. Furthermore, the secondary data analysis used includes the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), and Inverse Distance Weighted (IDW). The result showed that the Mempawah watershed has high, medium, and low flood risk potential. Areas with high flood potential have an area of 1,511,967 ha, those with medium potential were 2,606,778 ha, and the places with low potential were 12,644,034 ha. The changes in class user's accuracy results reached 90.909%, while those with no change were 83.333%. It was also discovered that when the satellite analysis was > 70%, it was regarded as good. This means that the accuracy of the interpretation results and flood change detection approach was also good.
 Keywords : Sentinel-2A; Estimation Potential; Flood Risk; Watershed
 Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Soil erosion risk and sediment yield assessment with Revised Universal Soil Loss Equation and GIS: The case of Nesha watershed, Southwestern Ethiopia

This research was administered to spatially predict the soil loss rate of the kaffa zone using a model estimate and GIS. Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was accustomed estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using DSMW soil map, vegetation cover (C) using spot6 images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P) using DEM and satellite images. supported the analysis, the mean and total annual soil loss potential of the study area was 30 tons ha-1 year-1 and 36,264.5tons ha-1 year-1, respectively. The results also showed that about 2.89, 8.02, 15.31 and 73.78% of the study area were classified as slight, moderate, high, and very high with values ranging from 0 to 15,15 to50,50 to 200, and >200 tons ha-1 year-1, respectively. The study demonstrates that the RUSLE using GIS and RS provides a great advantage to spatially analyzing multi-layer of knowledge. The expected amount of soil loss and its spatial distribution could facilitate sustainable land use and management.

Enigmatic annular landform on a Miocene planar karst surface, Nullarbor Plain, Australia

AbstractThe Nullarbor Plain is a ~200,000 km2planar karst surface in southern Australia, underlain by Cenozoic shallow‐water limestones. During the Miocene the area was uplifted, and although the plain is generally considered extremely flat, locally, the geomorphology of the Nullarbor Plain retains evidence of earth surface processes across a long, middle Miocene‐to‐present time span. The accessibility of the recent 0.4 arc‐second TanDEM‐X digital elevation model (DEM) by the German Aerospace Centre motivated the search for other possible fine‐scale landforms that would previously have been unresolvable. The analysis of DEM images revealed an enigmatic annular landform with an outer diameter between 1200 m and 1300 m. It consists of a circular elevated rim and a central dome. Its morphology is distinct from other landforms observed on the plain, and cannot be readily explained as a part of known active or inferred/expected processes on the plain (e.g., fluvial, aeolian, karst, tectonic or extra‐terrestrial impact processes).More recent karst processes (dayas formation) overprinting the landform indicates the presence of the annular structure prior to dayas formation. A unique microbial boundstone facies sampled from the bed‐rock of the annular structure supports an interpretation of long‐lived, at least partial retention of a primary depositional structure. Differential carbonate deposition, especially bioherm growth, was a likely origin of the annular topographical expression of the present‐day landform and is comparable to biogenic structures in the modern day deeper Great Barrier Reef.

수치표고모델을 이용한 지형 레이다 반사 면적 생성 및 바이스태틱 SAR 영상합성 기술 연구

In this study, we simulated target data for bistatic synthetic aperture radar (SAR) using a digital elevation model (DEM), raw data acquisition, and image synthesis. Using the DEM, target data similar to the actual topography could be generated. In particular, the reflectivity of the target was calculated from the geometrical structure between the SAR and the target. To verify and estimate the proposed method, we generated target data using bistatic SAR system modeling and simulated raw data acquisition. Finally, we compared the DEM and SAR images synthesized using the polar format algorithm (PFA).

A GIS Based Land Suitability and Gross Value Evaluation of Mined Lands in Şanlıurfa District

Landmined areas are located along the borders of Şanlıurfa province between Turkey and Syria with 220 km length and 400 m width. Governmental institutions attempted to determine the potential and suitability of mined lands for any agricultural activities before removing active mines. The research was conducted to estimate land suitability and gross value for irrigated agricultural production (GVIAP) of the mined areas using the database for the surrounding land and data obtained by Geographic Information Systems (GIS) techniques. The study was conducted in three steps; data including soil characteristics, digital elevation model (DEM) and orthophoto images of lands located 2 km nearby the mined land along the Turkish border were collected. The data containing DEM and orthophoto images of mined and adjacent lands were integrated in the second step. Finally, a field survey was conducted along the border district and soil samples were collected for each soil boundary nearby the mined lands. The integration of data in GIS allowed to expand the soil boundaries from adjacent lands into the mined lands. Data analysis showed that the total mined land is 6706 hectares of which 90.2% is highly and moderately suitable, 6.9% is less suitable and 2.9% is not suitable for irrigated agriculture. The gross value of irrigated agricultural production was estimated as 2212 US Dollars per hectare. The results revealed that the integration of inaccessible land database with the remote sensing data and GIS can be used to estimate the gross value which is derived from soil characteristics of the mined lands.

The Impacts of a Large Water Transfer Project on a Waterbird Community in the Receiving Dam: A Case Study of Miyun Reservoir, China

As natural wetlands are degrading worldwide, artificial wetlands can operate as a substitute to provide waterbirds with refuge, but they cannot replace natural wetlands. Reservoirs, one of the most common artificial wetlands in China, can be of great importance to waterbirds. Miyun reservoir in Beijing, China, has undergone a process similar to a natural lake being constructed in a reservoir. In this study, we surveyed waterbird community composition and evaluated the corresponding land cover and land use change with satellite and digital elevation model images of both before and after the water level change. The results showed that in all modelled scenarios, when the water level rises, agricultural lands suffer the greatest loss, with wetlands and forests following. The water level rise also caused a decrease in shallow water areas and a decline in the number and diversity of waterbird communities, as the components shifted from a shallow-water preferring group (waders, geese and dabbling ducks) to a deep-water preferring group (most diving ducks, gulls and terns). Miyun reservoir ceased to be an important waterbird habitat in China and is no longer an important stopover site for white-naped cranes. A similar process is likely to occur when a natural lake is constructed in a reservoir. Therefore, we suggest that policymakers consider the needs of waterbirds when constructing or managing reservoirs.

A Novel Algorithm to Delineate Surface Water Paths on Digital Elevation Model Image With Boundary Element Method

A Novel Algorithm to Delineate Surface Water Paths on Digital Elevation Model Image With Boundary Element Method

Selection of a Landing Site in the Permanently Shadowed Portion of Lunar Polar Regions Using DEM and Mini-RF Data

Direct sampling has never been performed in the permanently shadowed regions (PSRs) of lunar poles up to now. In the Chinese Chang’e-7 (CE-7) mission, a mini-flyer will fly from the lander in a solar illuminated region at the lunar south polar region to the nearby PSRs to collect samples for analysis. In this letter, four potential craters of the lunar south pole, including Shackleton, Shoemaker, de Gerlache, and Slater are discussed for this proposal. Design principles of the landing site, sampling site, and flight route are presented. The local surface slopes are calculated using a digital elevation model (DEM) to select a flat area as a potential landing site, which should allow ample time for solar illumination to support the rover from the lander and allow the flyer to reach the neighboring PSRs. Mini-RF data are applied for further validation of the flat landing and sampling sites, particularly for some rocky rough surfaces that are not identified in DEM and optical images of PSRs. The craters de Gerlache and Slater are found to be suitable for further analysis when high-resolution synthetic aperture radar (SAR) data are acquired by the new polarimetric SAR carried by CE-7.

Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

Mapping geologic structures from Gravity and Digital Elevation Models in the Ziway-Shala Lakes basin; central Main Ethiopian rift

This study attempts to delineate subsurface lineaments for the tectonically and volcanically active region of the Ziway-Shala Lakes basin, central Main Ethiopian rift. Most of the previously mapped subsurface structures in the region under consideration focus on delineating crustal structures thicknesses and Moho depths undulations. Moreover, surface structures in the same region were mapped using analysis of Digital Elevation Model image data. On the other hand, there are few studies that have targeted in mapping geologic structures lying at depth levels between the shallower and deeper subsurface. The objective of this research is thus to map the subsurface geologic structures/lineaments to an average depth of 3 km (crystalline basement layer depth) from surface using gravity data. These investigation results are validated by Digital Elevation Model extracted lineaments. Filtering techniques including derivative filters, upward-continuation and line module algorithm of PCI Geomatica are used to extract the gravity and topographic lineaments of the region. Orientation analyses of these subsurface and surface lineaments are made using line direction histogram of the QGIS software. Accordingly, the gravity subsurface lineaments mapped in this study are found to be dominantly oriented in the NNW-SSE to NW-SE and E-W direction on average. These results appear to be contrary to the NNE-SSW to NE-SW trending surface geologic structure mapped on the bases of actual field observation carried out by previous researchers and automatically extracted lineaments based on Digital Elevation Models data considered in this research. The subsurface lineaments mapped using gravity data are believed to govern groundwater dynamics within the basin and the adjacent basins in the area. These structural lineaments which are considered to be masked in the subsurface coincide with the orientation of the Mesozoic Ogaden rift as compared to the overlying surface structures which appear to coincide with the orientation of the Cenozoic Main Ethiopian rift.

Pengaruh Aktivitas Tektonik Terhadap Rekonstruksi Jalan Di Desa Pekan Gedang Dan Sekitarnya, Kecamatan Batang Asai, Kabupaten Sarolangun, Jambi

The Semangko Fault is a regional fault stretching along the island of Sumatra, consisting of several segments with movements of 6-37 mm per year (Sieh et al., 2000). Administratively, the research area is located in Batang Asai District, Sarolangun Regency, Jambi. Based on its geographical location, the study area is included in the Semangko Fault zone with geological conditions composed by pre-Tertiary age rocks that have been deformed, both faults and hefty. Deformation that occurs continuously, is characterized by several reconstructions of damaged roads in the alignment zone. The analysis carried out on the DEM (Digital Elevation Model) image includes morphotectonic aspects and identification of the solid data and fault fields collected in the field in order to interpret the level of tectonic activity that affects the road reconstruction in the area. From the results of the analysis it was identified that tectonic activity in the area was very high according to the classification of El Hamdouni et al. (2007).

A Novel Approach for Generation and Visualization of Virtual 3D City Model Using Open Source Libraries

Virtual 3D city models are digital representation of terrain surfaces, sites, buildings, roads, waterways, etc. In general, these models are used for presenting, exploring and analyzing the urban areas. These models are also used in domains of smart city, virtual reality, infrastructure planning, telecommunication, disaster management, real estate services, education, tourism and change detection. There are various approaches and commercial packages to generate virtual 3D city models. In this novel approach, we have not used any commercial package or tool toward generation and visualization of city model but instead developed in-house routines and used libraries from open source domain to generate and visualize real-time virtual 3D city models. Generation of 3D city models involves the imagery from high-resolution satellite, high-resolution digital elevation model (DEM) and vector shape files from open street maps (OSM) ( https://www.geofabrik.de/geofabrik/openstreetmap.html ) as inputs. Over and above virtual 3D city models, OSM vector layers of road network, river network and important places have been overlaid for making the virtual city model more informative. Toward realism, virtual trees are placed in the open spaces (as per shape file markings), Sun position and day/night effects, ambient condition effects and building texture, has been incorporated. To achieve generation of city model, we mosaicked preprocessed DEM scenes and images, resampled given datasets using cubic-resampling algorithm, registered the image, DEM and vector layers using phase correlation algorithm, and extracted the height information by processing of DEM and shape files. Vector layers are registered using latitude and longitude and overlaid over the 3D city model for information about the places, roads and water bodies. For efficient visualization and rendering of the model, open scene graph library and tiling-based approach are used in which tiles are replaced in the main memory by its sub-tiles based on viewer’s position. Mentioned approach is applicable to generate and visualize city model of any given area provided the DEM, imagery and OSM data. This paper mainly discusses the approach and advantages of this new cost-effective approach to develop virtual 3D city models using Indian Remote Sensing datasets.

Coarse-to-fine waterlogging probability assessment based on remote sensing image and social media data

ABSTRACT Urban waterlogging probability assessment is critical to emergency response and policymaking. Remote Sensing (RS) is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre- and post-disaster RS images. However, RS images are usually limited to the revisit cycle and cloud cover. To solve this issue, social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster, which leads itself a compensation for RS images. In this paper, we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data, near real-time RS image and historical geographic information, in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability. Firstly, to generate a coarse waterlogging probability map, the historical inundated areas are derived from Digital Elevation Model (DEM) and historical waterlogging points, then the geographic features are extracted from DEM and RS image, which will be input to a Random Forest (RF) classifier to estimate the likelihood of hazards. Secondly, the real-time waterlogging-related information is extracted from social media data, where the Convolutional Neural Network (CNN) model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel. Finally, fine waterlogging probability map scan be generated based on morphological method, in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect. The 2016 Wuhan waterlogging and 2018 Chengdu waterlogging are taken as case studies to demonstrate the effectiveness of the proposed framework. It can be concluded from the results that by integrating RS image and social media data, more accurate waterlogging probability maps can be generated, which can be further applied for inundated areas identification and disaster monitoring.

STRUKTUR GEOLOGI DAN VULKANOSTRATIGRAFI; PENDEKATAN MODEL ELEVASI DIGITAL DAN CITRA LANDSAT 8

Morphological change and deformation have been happen on natural landscape on volcanic complex. Their would been seen topography formation the give contour line expression, drainage pattern as especially character for understanding geological guideline such as structural geology and lithology. In the geological and geophysics on volcanic complex can use early methode with Digital Elevation Model (DEM) of research scale map and Landsat Image 8 for understanding structural geology and volcanostratigraphy. Research study on Arjuno Welirang Volcanic Complex, East Java, a part of active volcano in Indonesia. This study used scale map 1:25.000 wich analyzed of topography contour and drainage pattern for determination of geological feature as structural geology and volcanostratigraphy unit. Coherence of dense contour line is an igneous rock lava indicated, whereas distantly space contour line is an indicated of pyroclastic. DEM is a product extraction processes of contour topography. MED dan Citra Landsat 8 could help deliniaton of structural geology and volcanostratigraphy unit, which analyzed of morphology, drainage pattern, source of eruption, morphogenesis, and lithology remark. Based on both of image model on KGAW have dextral fault with oriented of northwestern-southeastern, sinistral fault with oriented of southwestern-northeastern, and then structural volcanic as caldera, crater, and debris avalanche. In the KGAW have five source of main eruption and the the other eruption surrounding of KGAW and their become is composed of volcanostratigraphy unit. Approach to using of DEM and Lendsat Image 8 are a part of early stage on for understanding structural geology and volcanostratigraphy on volcanic complex.

Identify the Archaeological Research of Thatta District Through Geo-Spatial Technologies: A Case Study of Makli Graveyard and Banbhore Fort

Archaeological studies with the help of geographic information systems and remote sensing have been used in temporal, spatial, regional analysis and to investigate traditional and historical ways of human life. Remote sensing alludes to a wide variety of high-technology methods for collecting data pertaining to the physical or chemical properties of an archaeological site survey. The aim of this study is to identify the archaeological site of Makli graveyard and Banbhore fort through satellite images and explore the major land cover patterns on the southern part of Sindh province using geospatial technologies. Additional goals are to evaluate and visualize the Digital Elevation Model (DEM) for the southern part of Sindh province. A landsat-8 OLI / TIRS of 20th December 2014 and a DEM image were used to classify land cover and artifacts at the site. The result indicates that historical monuments at Makli, and Banbhore fort, Thatta testify in an outstanding manner, to the civilization of the Sindh region. geographically, its location is vulnerable around the river. Banbhore has survived such threats and continued to flourish as the only and most important port of Sindh.

Volkanostratigrafi Inderaan Jauh Kompleks Gunungapi Gede dan Sekitarnya, Jawa Barat, Indonesia

Remote sensing technology greatly helps to identify the various of volcano features, including active, old and ancient volcanoes. The aim of this paper is intended to introduce various volcanic features in the Gede Volcano Complexs (GVC) and souronding area; compose volcanostratigraphy; and estimate the history of the volcanoes. The method used is a visual interpretation 9 meters spatial resolution of Digital Elevation Model (DEM) TerraSar-x image. Indonesian Stratigraphy Nomenclature Guide 1996 was implemented in vocanostratigraphy unit classification, involving Arc, Super Brigate, Brigate, Crown and Hummockly. Based on the interpretation the DEM image, volcanostratigraphic unit the Gede Volcano Complex consists of Bregade Masigit (Br. M.), which consists of Joklok (Gm.J.) and Gegerbentang (Gm.G.) Hummocs; Crown Lingkung (Kh.L.) consisting of Pangrango (Gm.P.), Situ Gunung (Gm Sg.), Cikahuripan (Gm.Ck.), Pasir Prahu (Gm.Ph) Hummocs; Gege Crown (Kh.G.), which is located in the east of Lingkung Crown. The Gede Crown consists of Gumuruh humock (Gm.Gh.), Gunung Gede lava flows (LG 1,2,3,4,5), and giant debrise avalances (gv-G). The geological mapping based volcanostratigraphy is very useful for exploration of mineral and energy resources, as well as geological hazards.Keywords : volcanostratigraphy, DEM TerraSar-x image, Gunung Gede Complexs.

Volkanostratigrafi Inderaan Jauh Kompleks Gunungapi Gede dan Sekitarnya, Jawa Barat, Indonesia

Remote sensing technology greatly helps to identify the various of volcano features, including active, old and ancient volcanoes. The aim of this paper is intended to introduce various volcanic features in the Gede Volcano Complexs (GVC) and souronding area; compose volcanostratigraphy; and estimate the history of the volcanoes. The method used is a visual interpretation 9 meters spatial resolution of Digital Elevation Model (DEM) TerraSar-x image. Indonesian Stratigraphy Nomenclature Guide 1996 was implemented in vocanostratigraphy unit classification, involving Arc, Super Brigate, Brigate, Crown and Hummockly. Based on the interpretation the DEM image, volcanostratigraphic unit the Gede Volcano Complex consists of Bregade Masigit (Br. M.), which consists of Joklok (Gm.J.) and Gegerbentang (Gm.G.) Hummocs; Crown Lingkung (Kh.L.) consisting of Pangrango (Gm.P.), Situ Gunung (Gm Sg.), Cikahuripan (Gm.Ck.), Pasir Prahu (Gm.Ph) Hummocs; Gege Crown (Kh.G.), which is located in the east of Lingkung Crown. The Gede Crown consists of Gumuruh humock (Gm.Gh.), Gunung Gede lava flows (LG 1,2,3,4,5), and giant debrise avalances (gv-G). The geological mapping based volcanostratigraphy is very useful for exploration of mineral and energy resources, as well as geological hazards.Keywords : volcanostratigraphy, DEM TerraSar-x image, Gunung Gede Complexs.